摘要: |
为研究纳米SiO2对溶蚀混凝土损伤的影响,选取浓度为2mol/L的NH4Cl溶液为侵蚀介质,对普通混凝土和纳米SiO2混凝土分别溶蚀0、4、9、28、45、64d,测试了试件的抗压强度耐蚀系数.利用核磁共振(NMR)技术、场发射扫描电镜(SEM)和热分析仪研究了试样的组织结构.应用灰色系统理论建立了混凝土寿命GM(1,1)预测模型.结果表明:普通混凝土和纳米SiO2混凝土的抗压强度均随溶蚀龄期的延长而不断降低;纳米SiO2有效提高了混凝土在服役水环境条件下的结构可靠度,纳米SiO2混凝土抗压强度耐蚀系数比普通混凝土高1104%;纳米SiO2可有效改善混凝土微结构缺陷,使微观结构更加致密,减缓了侵蚀介质在混凝土内部的扩散传输速率;通过GM(1,1)模型预测得到纳米SiO2混凝土溶蚀寿命是普通混凝土的24倍. |
关键词: 纳米SiO2 混凝土 抗压强度 微结构 溶蚀 GM(1,1)预测模型 |
DOI:103969/j.issn.1007 9629202104013 |
分类号: |
基金项目:内蒙古农业大学博士科研启动基金资助项目(BJ2014 4) |
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Effect of Nano SiO2 on Corrosion Resistance and Corrosion Life of Concrete |
WANG Zongxi, YAO Zhanquan, HE Liang, WU Hanhan, LIU Zimei
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Water Conservancy and Civil Engineering College, Inner Mongolia Agricultural University, Hohhot 010018, China
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Abstract: |
In order to study the effect of nano SiO2 on the damage of corrosion concrete, 2mol/L NH4Cl solution was used as the erosion media to corrode ordinary concrete and nano SiO2 concrete for 0, 4, 9, 28, 45,64d respectively. The compressive strength corrosion resistant coefficient of the specimens were tested by hydraulic testing machine. The structures of the specimens were studied by nuclear magnetic resonance(NMR), field emission scanning electron microscopy(SEM) and thermal analysis. The GM(1,1) prediction model for concrete life was established using grey system theory. The results show that the compressive strength of ordinary concrete and nano SiO2 concrete decreases with the age of corrosion; Nano SiO2 effectively improves the structural reliability of concrete in service water environment, and the corrosion resistance coefficient of compressive strength of nano SiO2 concrete is 1104% higher than that of ordinary concrete; nano SiO2 can effectively improve the microstructure defects of concrete, make the microstructure more compact, and slow down the diffusion and transmission rate of the corrosive medium inside the concrete; It is predicted by GM(1,1) model that the corrosion life of nano SiO2 concrete is 24 times that of ordinary concrete. |
Key words: nano SiO2 concrete compressive strength microstructure corrosion GM(1,1)prediction model |